cuthberto's take on the semi-finals please be wrong on Wednesday........ state-space-models.github.io/cuthberto-ca...
Wait till he hears about cuthbert github.com/state-space-...
GitHub - state-space-models/cuthbert: State-space model inference with JAX
State-space model inference with JAX. Contribute to state-space-models/cuthbert development by creating an account on GitHub.
github.com
Emily✨@emilyoram.bsky.social · last mo.Giroud x Colin 🐛🍫🇫🇷
I think this didn't get nearly enough traction. Sam did some fantastic work here leveraging the library we've been developing to make predictions for football. This was ported to a UI by Ryan Chan (Warwick MSc student) who's doing his thesis with me (on the methods behind the prediction).
Sam Duffield@samduffield.com · 2mo agoThe World Cup is about to start, I wrote a model using cuthbert to predict the games, check out cuthberto-carlos 🐛⚽ ryantjx.github.io/cuthberto-ca...
The World Cup is about to start, I wrote a model using cuthbert to predict the games, check out cuthberto-carlos 🐛⚽ ryantjx.github.io/cuthberto-ca...
Co-organising a workshop: Non-Equilibrium Sampling: Diffusions · Flows · Particles September, Newcastle Come join the fun!
Newcastle Non-Equilibrium Sampling Workshop
Group photo from last year's Newcastle Sampling workshop.
sites.google.com
I'll be at ICLR this week 🇧🇷 presenting my complete SDE decomposition at the DeLTA workshop on Monday. Reach out if you want to meet!
The ensemble Kalman filter is now in cuthbert 🔥 The EnKF is one of those algorithms that "just works" - oftentimes in settings it has no right to github.com/state-space-...
Add the Ensemble Kalman Filter by DanWaxman · Pull Request #229 · state-space-models/cuthbert
This PR introduces the ensemble Kalman filter (EnKF). The implementation is based in part on the implementation of CD-Dynamax. That implementation, in turn, is inspired by Algorithm 10.2 in the not...
github.com
“Parallelizing MCMC Across the Sequence Length”: This one is really cool. statmodeling.stat.columbia.edu/2026/02/03/p...
“Parallelizing MCMC Across the Sequence Length”: This one is really cool. | Statistical Modeling, Causal Inference, and Social Science
statmodeling.stat.columbia.edu
New open source: cuthbert 🐛 State space models with all the hotness: (temporally) parallelisable, JAX, Kalman, SMC
New preprint! A Complete Decomposition of Stochastic Differential Equations I characterise *all possible SDEs* that satisfy given time-dependent marginals p(x,t)
Usual MCMC algorithms are typically guaranteed to work well when used to sample from target distributions for which i) mass is reasonably well-concentrated in the centre of the state space, and ii) the log-density is smooth and of moderate growth. Outside of this setting, things can go poorly.
New paper on arXiv! And I think it's a good'un 😄 Meet the new Lattice Random Walk (LRW) discretisation for SDEs. It’s radically different from traditional methods like Euler-Maruyama (EM) in that each iteration can only move in discrete steps {-δₓ, 0, δₓ}.
In slides from a recent talk - the { virtuous / vicious } cycle of filtering, smoothing, and parameter estimation in state space models.
Me: Hey so where’s good to eat round here? Singapore taxi driver: Malaysia
posteriors 𝞡 published at ICLR! I’ll be in Singapore next week, let’s chat all things scalable Bayesian learning! 🇸🇬👋
So simple! Normally we order our minibatches like a, b, c, ...., [shuffle], new_a, new_b, new_c, .... but instead, if we do a, b, c, ...., [reverse], ...., c, b, a, [shuffle], new_a, new_b, .... The RMSE of stochastic gradient descent reduces from O(h) to O(h²) arxiv.org/abs/2504.04274
Sequential Monte Carlo (aka. Particle Socialism?): "why send one explorer when you can send a whole army of clueless one"
Alex Thiery@alexxthiery.bsky.social · last yr.Next week is the MCMC chapter of my simulation course. Asked chatgpt to come up with a funny drawing:
Was revisiting the Neural ODEs paper the other day and greatly enjoying. But I found this super confusing, it’s not an A=B+A statement
Thrillingly (/s), I have today (lightly) updated my website (sites.google.com/view/sp-mont...). I highlight that I've added i) links to several slide decks for talks about my research, and ii) materials related to the (few) short courses which I've given in the past couple of years. Enjoy!
Sam Power's site
Hello! My name is Sam, and I am a researcher in Statistics. I am currently Lecturer in Statistical Science at the University of Bristol. Prior to this role, I was a Senior Research Associate (also at...
sites.google.com
Hi there! This account will post about the AlgoPerf benchmark and leaderboard updates for faster neural network training via better training algorithms. But let's start with what AlgoPerf is, what we have done so far, and how you can train neural nets ~30% faster.
I've been using Cursor and enjoying it but I'm not sure I'm bullish on it for the long run. One of the best parts about VSCode is the ecosystem of extensions (and that it is open source). Cursor is already out of sync and having issues with extension compatibility
Unexpected indent when using "Run selection in terminal"
It seems this issue can be resolved by using the latest version of Python extensions when using Python 3.13. Currently in Cursor, we can only use outdated versions (at least 6 months old) of Python, P...
forum.cursor.com
Thermo Matrix Exponentials has been published in Physical Review Research 🔥 On a thermodynamic computer, the matrix exponential occurs very naturally through the temporal covariance driven by the noise - a polynomial speedup over digital computers!
Thermodynamic matrix exponentials and thermodynamic parallelism
Thermodynamic computing exploits fluctuations and dissipation in physical systems to efficiently solve various mathematical problems. It was recently shown that certain linear algebra problems can be ...
journals.aps.org
Discovered today that LinkedIn has daily “Puzzle games” and they’re quite fun!
Thermodynamic Linear Algebra is published! And I, for one, am delighted to see work from Normal Computing published in Unconventional Computing 😝
Thermodynamic linear algebra - npj Unconventional Computing
npj Unconventional Computing - Thermodynamic linear algebra
nature.com
Parallel scans accumulate sequences on GPUs (or other parallel hardware) at logarithmic cost in the size of the input. The canonical example is cumulative sums (a, a+b, a+b+c, ...) from an input (a, b, c, ...), but this is hardly the only use, and, e.g., Kalman filtering can be handled in parallel.
